Research

Job Market Paper

Inside the black box: Neural network-based real-time prediction of US recessions (single-authored)[Lastest version] 

Feedforward neural network (FFN) and two specific types of recurrent neural network, long short-term memory (LSTM) and gated recurrent unit (GRU), are used for modeling US recessions in the period from 1967 to 2021. The estimated models are then employed to conduct real-time predictions of the Great Recession and the Covid-19 recession in US. Their predictive performances are compared to those of the traditional linear models, the logistic regression model both with and without the ridge penalty. The out-of-sample performance suggests the application of LSTM and GRU in the area of recession forecasting, especially for the long-term forecasting tasks. They outperform other types of models across 5 forecasting horizons with respect to different types of statistical performance metrics. Shapley additive explanations (SHAP) method is applied to the fitted GRUs across different forecasting horizons to gain insight into the feature importance. The evaluation of predictor importance differs between the GRU and ridge logistic regression models, as reflected in the variable order determined by SHAP values. When considering the top 5 predictors, key indicators such as the S&P 500 index, real GDP, and private residential fixed investment consistently appear for short-term forecasts (up to 3 months). In contrast, for longer-term predictions (6 months or more), the term spread and producer price index become more prominent. These findings are supported by both local interpretable model-agnostic explanations (LIME) and marginal effects.

Working Papers

Real-time Prediction of the Great Recession and the Covid-19 Recession (single-authored) [Lastest version] 

A series of standard and penalized logistic regression models is employed to model and forecast the Great Recession and the Covid-19 recession in the US. These two recessions are scrutinized by closely examining the movement of five chosen predictors, their regression coefficients, and the predicted probabilities of recession. The empirical analysis explores the predictive content of numerous macroeconomic and financial indicators with respect to NBER recession indicator. The predictive ability of the underlying models is evaluated using a set of statistical evaluation metrics. The results strongly support the application of penalized logistic regression models in the area of recession prediction. Specifically, the analysis indicates that a mixed usage of different penalized logistic regression models over different forecast horizons largely outperform standard logistic regression models in the prediction of Great recession in the US, as they achieve higher predictive accuracy across 5 different forecast horizons. The Great Recession is largely predictable, whereas the Covid-19 recession remains unpredictable, given that the Covid-19 pandemic is a real exogenous event. The results are validated by constructing via principal component analysis (PCA) on a set of selected variables a recession indicator that suffers less from publication lags and exhibits a very high correlation with the NBER recession indicator.

Work in Progress

Probabilistic Forecast Combinations in Real-time Prediction of US Recessions (with Prof. Dr. Jens Krüger)